How Stock Analysis Has Changed
The four ways investors analyse a stock – fundamental, technical, quantitative and passive – have kept their names for decades. What changed is the data, the speed, and who can reach the tools. This free, interactive guide walks through each method, lets you run a working version yourself, and shows exactly what to look for.
What you’ll learn
- The four lenses every serious investor uses, and how they fit together
- How a discounted cash flow valuation swings on its assumptions – run one live
- How moving averages, RSI and the golden/death cross read price
- How a quant factor model ranks a whole market, and how the weighting changes the winner
- Why every factor tilt trades conviction against concentration
Who it’s forFor anyone moving from following the markets to actually analysing them. No finance background needed – every method is taught from first principles with a worked, interactive example.
Four ways to look at the same company
Ask three serious investors how to analyse a stock and you will get three answers, because they are looking through different lenses. The lenses themselves are decades old. What matters is knowing what each one is actually for, and that most practitioners now blend them rather than pick a side.
Fundamental
Studies revenue, earnings, cash flow, margins and the balance sheet to ask one question: what is this business actually worth? The home of discounted cash flow and the long-term, conviction-led investor.
Technical
Ignores the business and reads the price and volume instead, looking for patterns in crowd behaviour. Moving averages, RSI and support/resistance. Favoured for timing rather than selection.
Quantitative
Turns judgement into rules a model can run across thousands of stocks at once. Mathematical, systematic, repeatable. Once the preserve of hedge funds, now within reach of anyone with a spreadsheet.
Passive & Factor
Instead of picking winners, buys broad baskets or deliberately tilts toward measurable traits, value, momentum, quality, size. Academic theory that became the biggest structural shift of the decade.
First the story, then the controls
Start with the shift in plain terms, then scroll down and drive each method yourself. Every demo tells you what you are doing, gives you an output that responds to your inputs, and ends with what to look for.
Hand-built spreadsheets
Analysts keyed figures from PDF filings into Excel by hand. A single DCF could take a day, and data was expensive and gated behind terminals.
Filings read by machines
Free APIs deliver clean financials in seconds, and language models summarise a 200-page filing before you have finished your coffee. The judgement, not the typing, is the work.
Charts drawn by hand
Trend lines were drawn on paper or in clunky desktop software. Indicators were calculated manually or watched on delayed feeds.
Indicators for free, live
Any browser charts unlimited tickers with dozens of indicators in real time, at no cost. The edge moved from access to interpretation.
The hedge-fund preserve
Systematic models needed costly data, server rooms and PhDs. Effectively closed to anyone outside an institution.
A laptop and Python
Free libraries, cheap compute and open data let an individual backtest a factor model from a kitchen table. The gap is skill, not budget.
Index funds, full stop
Passive meant a plain market-cap index fund. “Factors” lived in academic papers, not products you could buy.
Smart-beta on tap
Thousands of low-cost ETFs package single factors or blends. You can build a tilt toward value or momentum with a few clicks.
Now drive them yourself
Four methods, four working demos. Move the controls and watch the output respond. The data is illustrative, but the maths underneath is the real thing.
The DCF reactor
What you are doing: estimating what a business is worth by projecting its free cash flow five years out, adding a terminal value for everything after, and discounting it all back to today. The sample company throws off £1,000m of free cash flow now and has 500m shares. Move the three assumptions and watch the fair value per share react.
Nudge the discount rate down by just 1% and watch the valuation jump. A DCF is only as solid as its assumptions, and small changes to numbers you cannot know with certainty swing the answer wildly. That fragility is the lesson, not the precise figure.
The signal chart
What you are doing: overlaying the classic technical toolkit on one illustrative price series, roughly two years of daily closes. Toggle each layer on and off. The golden cross (50-day average rising above the 200-day) and death cross (the reverse) are marked automatically.
Watch where the short average crosses the long one and what price does afterwards. Technicals describe what a crowd has done, not what it must do next. Note too how the crosses arrive after the move is well underway, the signal lags the turn it is meant to call.
The factor screen
What you are doing: running a simple quant model across ten illustrative companies. Each is scored on value (cheaper is better), momentum (recent strength) and quality (return on equity). You set how much each factor matters, and the model re-ranks the whole universe instantly.
Crank one weight to the top and the leaderboard reshuffles completely. A quant model is only as opinionated as its weights, and there is no neutral choice. This is exactly what a fund is doing, just across thousands of names at once.
| # | Illustrative company | P/E | 12m momentum | ROE | Score |
|---|
The tilt builder
What you are doing: building a factor-tilted portfolio. The four sliders are normalised to 100% of your exposure, so leaning into one factor automatically means owning less of the others. There is no such thing as a free tilt.
Every tilt is a bet. A balanced spread spreads your risk; a heavy lean concentrates it. Push any single factor past half your exposure and you are no longer diversified, you are making one concentrated wager dressed up as a portfolio.
The names stayed. The engine was rebuilt.
Click along the timeline. Each step is a genuine shift of the past decade, with a concrete example of it in action and the outcome an investor would actually look for.
A blended workflow you can actually run
The modern investor rarely picks one lens. Here is how the four fit together into a single repeatable process, using the same free tools that did not exist a decade ago.
Screen with the quant lens
Start wide. Use a free screener to filter the whole market down to a shortlist on value, quality and momentum, exactly the factors you weighted in the demo above. You have just done in seconds what once took a team.
Read the business with fundamentals
Take your shortlist and go deep on the numbers. Pull the financials from a free API, and let an AI summary flag the risks buried in the filing, then build your own DCF and stress-test the assumptions the way the reactor showed.
Time the entry with technicals
Conviction tells you what; technicals help with when. Check the trend and the moving averages so you are not buying into a falling knife. The signal lags, so treat it as context, not gospel.
Size the position like a portfolio
Decide how much to own, and against what. The tilt builder’s lesson applies: a single great idea held too large is a concentrated bet. Balance conviction against the risk of being wrong.
Tap a card to flip it
Six quick prompts to test what stuck. No score, no sign-in, just a self-check before you go.
Fundamental analysis. It studies cash flow, earnings and the balance sheet, and is the home of the DCF you ran in the reactor.
When the 50-day average rises above the 200-day. A widely watched technical signal, though it confirms a move rather than predicting one.
The discount rate. A 1% change can move the valuation dramatically, which is why a DCF teaches humility as much as precision.
A new class of input collected outside the company, satellite images of car parks, card spend, web traffic, used to read demand before the official numbers land.
Because exposure sums to 100%. Leaning hard into one factor means owning less of everything else, turning diversification into a single concentrated bet.
Not the methods, but the inputs, speed and access. Tools once gated behind institutions now sit in any browser, for free.
The one thing to carry away
The four lenses are as relevant as ever, and serious investors blend them rather than pick a tribe. Fundamentals for what to own, technicals for when, quant to do it at scale, and factor thinking to size the whole thing sensibly.
What a decade of technology changed is who gets to play. The expensive data, the models, the live charts, the filing analysis, all of it now sits within reach of an individual with a laptop. The edge has moved from access to judgement, and judgement is the one thing you can keep building.